Tag: digital transformation

AI Doesn’t Know Our Business. We Do.

AI can prototype our workflow. It can draft our requirements. It can generate our user stories. But it has no idea how our business actually works.

It can’t tell us where the bottlenecks in the process are. Our loan officer knows that. It can’t explain why the workaround exists. Our operations manager knows that. It doesn’t know which shortcuts blow up in an audit. Our compliance analyst knows that. It has no idea where customers rage-quit. Our customer service supervisor knows that.

Our best people, the ones who actually know how the work works, often have trouble translating what’s in their heads into something a developer can build. They know where the friction is. They know which exceptions happen every Thursday afternoon even though the procedure says they shouldn’t. They know which approval step matters, and which one is just a bureaucratic leftover from a reorg three years ago.

For the first time, the people who do the work and understand the problems they’re trying to solve can start shaping the solutions. AI gives their expertise a doorway it never had before.

These subject matter experts can sit down with an AI tool and start turning what they know into something visible. Plain language in. Workflow out. Prototype sketched. Edge cases surfaced. User stories drafted.

And when they do that, something shifts. They realize software development isn’t magic. It’s a thousand small decisions that someone has to make. That realization alone is worth the exercise.

Instead of walking into a meeting saying, “we need a dashboard” or “can we automate this?” they walk in with something tangible. Rough? Sure. Wrong in places? Probably. But it doesn’t have to be production-ready. It has to be conversation-ready.

That changes everything.

Developers, architects, and project leaders can see the idea. They ask sharper questions. They spot what already exists, what creates risk, what can ship fast. The subject matter expert starts understanding what building a solution actually involves. The dependencies. The data quality landmines. The difference between a slick mockup and something that holds up in production.

That shared understanding transforms the relationship between business and technology.

We know the old pattern. Business has a need that’s difficult to explain, the technology team tries to interpret it, weeks pass, something appears, the business says, “close but not what we meant,” the cycle repeats. Everyone gets frustrated. Nothing ships.

AI doesn’t kill that cycle. It compresses and turbo charges it. When developers start with a real prototype and a real conversation, iterations can take hours or days instead of weeks.

The AI win is getting people in the same room faster, with something real to react to.

If more people can generate ideas, workflows, and prototypes, we’ll start getting more possibilities than we can pursue. A good problem to have, but still a problem.

Bottom-up energy is powerful. It surfaces solutions from the people closest to the work. It finds problems leadership didn’t know existed.

But without focus, we’ll drown in prototypes. The bottleneck doesn’t disappear. It moves from “we don’t have enough ideas” to “we have no idea which ideas deserve investment.”

That’s on leadership.

Executives, your job isn’t to be the gatekeeper of imagination. Play that role and the old problems come back fast. Good ideas will die in departments, in notebooks, in hallway conversations that never go anywhere. You become the reason nothing changes.

Your job is to be the steward of focus. Create the channel. Invite ideas up. Encourage people to explore, prototype, get specific. Then make the call on what moves forward.

Bottom-up imagination. Technical refinement. Executive focus. That’s a model that AI tools make possible.

Subject matter experts bring better ideas because AI helps them say what they know. Technical teams sharpen those ideas because they understand what durable software is. Executives look across the whole landscape and ask the hard questions nobody else is positioned to ask.

Does this solve a real problem or just an annoying one? Does it scale? Does it duplicate something we already own? Does it create risks we can’t absorb? Is this a strategic investment or a distraction dressed up as innovation?

Not every prototype becomes a project. Not every project deserves to live in our permanent technology environment. That’s not a failure of imagination. That’s how imagination gets distilled down to create tangible business value.

The future advantage won’t go to the fastest movers or the biggest tool buyers. It won’t go to the organizations that treat AI like a cure-all and wonder why nothing changes.

It’ll go to the ones that know their business well, listen hard to the people doing the work, and never confuse creating more things with creating better things.

AI can help us build faster. That’s the easy part.

Knowing what to build and why. That’s still on us.

Photo by BEN ELLIOTT on Unsplash – This is a downwind leg, spinnakers out, boats grabbing everything the wind has to offer. A good metaphor for AI. It can give us remarkable speed. But we’re still in a race, there’s still a course to navigate, and the turns are still ours to make.

The Adoption Curve in Real Life (It’s Messier than the Textbooks Say)

You’ve probably seen it happen. A new tool explodes across your social media feeds, your team starts asking questions, and you’re left wondering whether to embrace it or ignore it. Last month’s OpenClaw rollout is the latest reminder of how chaotic technology adoption really is.

Technology adoption curves are depicted as neat, predictable diagrams, a smooth line moving from innovators to early adopters to the early majority and eventually to late adopters.

In textbooks, the curve looks calm. In real life, it feels more like a storm.

Watching the recent surge of interest around OpenClaw, an open-source AI automation tool that lets developers and non-developers build custom autonomous agents, highlights this contrast clearly.

The tool moved rapidly from Clawdbot to MoltBot to OpenClaw. While its identity was in motion, innovators and early adopters embraced it with enthusiasm. Within days, countless articles and YouTube videos appeared with reviews, tutorials, and predictions about how it would reshape everything.

Within another week, we began hearing a more complete message. People still praised its power, but they also surfaced significant security weaknesses and vulnerabilities that accompany those capabilities.

My goal in this post is less about celebrating OpenClaw itself and more about understanding the real-world adoption pattern that I’ve seen countless times.

Phase 1: The Enthusiasts Light the Fuse

Early adopters jump in first. They’re curious, energetic, and quick to celebrate what they’ve discovered.

They imagine what could be, long before most people fully understand what exists today. They test edge cases, build experiments, share demos, and push boundaries simply because the possibility fascinates them.

This group rarely waits for permission. Their momentum gives a new idea its initial lift.

Phase 2: Quiet Experimenters Emerge

Close behind them comes a second tier of users who watch carefully and learn before speaking.

They begin to explore the tool in private, trying things on their own terms rather than joining the public conversation. Their silence can look like hesitation but usually signals careful attention and research.

They want confidence before committing.

Phase 3: The Tribalization of Opinion

At the same time, people who barely understand the technology start lining up on all sides of the debate as if it were a political issue.

Some declare that it will transform everything. Others warn that it is reckless or dangerous. Still others dismiss it as a passing fad.

Much of this reaction grows from identity, fear, or ideology rather than direct experience. The conversation gets louder while genuine clarity is harder to find.

Phase 4: Rapid Evolution and Ecosystem Growth

If the tool has real potential, the surrounding environment begins to move quickly.

The creators ship frequent updates of their new product. Early adopters invent new uses that nobody predicted. Supporting products (like Cloudflare services or the Mac Mini in the case of OpenClaw’s recent meteoric growth) suddenly see rising demand because they pair well with the new capability. Other companies look for ways to add integrations that make the new tool easier to plug into existing systems.

At this stage, the story shifts from a single product to an emerging ecosystem that amplifies its reach.

Phase 5: The Backlash from the Pioneers

Then a familiar turn arrives.

Some early adopters start getting bored and even a little disillusioned. Others start pointing out limitations, rough edges, and frustrations that were overlooked during their initial excitement. Sometimes they simply move on to the next shiny thing. Other times, sustained use reveals real constraints that only time can expose.

Ironically, the quieter second wave adopters are just beginning to feel comfortable. Enthusiasm and skepticism overlap in the marketplace.

Phase 6: Corporations Hit the Brakes

Meanwhile, large organizations watch from the sidelines while asking serious questions about security, governance, and risk. They focus on oversight, accountability, and long-term stability.

From a leadership perspective, this cautious approach seems safe. They can’t risk the family jewels on a promise of something amazing. At least, not yet.

Phase 7: The Safe Version Arrives

If the capability truly matters and maintains momentum, a major platform provider such as Microsoft, Google, Amazon, (and nowadays) OpenAI, or Anthropic eventually releases something comparable inside their own infrastructure.

This can happen through acquisition, partnership, or independent development. When it does, the risk profile shifts almost overnight.

What once felt experimental and dangerous now feels enterprise-ready. It’s the signal that many CIOs and CISOs were waiting for.

Phase 8: The Irony of Timing

By the time most corporations adopt the new “safer version” of the capability, the original pioneers have already moved on.

They’re chasing the next breakthrough and speaking about the earlier tool as if it belongs to another era. Six months earlier it felt magical. Now it feels ordinary, in part because that earlier innovation did its job of pushing the frontier outward.

What This Means for Leaders

For leaders who care about both capability and security, sprinting toward the bleeding edge rarely makes sense.

Waiting for stability, clear governance, and trusted integration usually serves organizations better. In practice, that means allowing major, “trusted” platforms to bring new capabilities inside their own secure environments before moving at scale.

At the same time, leaders can’t afford to look inward only. Something important is always unfolding beyond the walls of their organization. Entrepreneurs are experimenting. Startups are forming. New approaches and new possibilities are taking shape. If a company becomes too passive or too comfortable, it risks being outpaced rather than protected.

The real leadership challenge is learning to tell the difference between waves that will reshape an industry and those that will fade.

Some signs of staying power are multiple independent developers building on top of a new technology, respected technologists moving beyond flashy demos into real production use cases, and serious enterprise concerns about security and governance being addressed rather than dismissed.

We don’t need to chase every new wave.

The real test is recognizing the waves that matter before they feel safe enough to bring inside our organization.

Photo by Nat on Unsplash – Innovation is easy to see. Truth is harder to judge.     

Strategy First. AI Second.

Eighty-eight percent of AI pilots fail to reach production, according to IDC research. Most fail because organizations chase the tool instead of defining the outcome. They ask, “How do we use AI?” rather than “What problem are we solving?”

A little perspective

I’m old enough to remember when VisiCalc and SuperCalc came out. That was before Lotus 1-2-3, and way before Microsoft Excel. VisiCalc and SuperCalc were just ahead of my time, but I was a big user of Lotus 1-2-3 version 1. Back then, everyone focused on how to harness the power of spreadsheets to change the way they did business.

Teams built massive (for that time) databases inside spreadsheets to manage product lines, inventory, billing, and even entire accounting systems. If you didn’t know how to use a spreadsheet, you were last year’s news.

The same shift happened with word processing. Microsoft Word replaced WordPerfect and its maze of Ctrl and Alt key combinations. Then the World Wide Web arrived in the early 1990s and opened a new set of doors.

I could go on with databases, client-server, cloud computing, etc. Each technology wave creates new winners but also leaves some behind.

The lesson is simple each time. New tools expand possibilities. Strategy gives those tools a purpose.

The point today

AI is a modern toolkit that can read, reason (think?), write, summarize, classify, predict, and create. It shines when you give it a clear job. Your strategy defines that job. If your aim is faster cycle times, higher service quality, or new revenue, AI can be the lever that helps you reach those outcomes faster.

Three traps to avoid

Tool chasing. This looks like collecting models and platforms without a target outcome. Teams spin up ChatGPT accounts, experiment with image generators, and build proof-of-concepts that fail to connect to real business value. The result is pilot fatigue. Endless demonstrations with no measurable impact.

Shadow projects. Well-meaning teams launch skunkworks AI experiments without governance or oversight. They use unapproved tools, expose sensitive data, or build solutions that struggle to integrate with existing systems. What starts as innovation becomes a compliance nightmare that stalls broader adoption.

Fear-driven paralysis. Some organizations wait for perfect clarity about AI’s impact, regulations, or competitive implications before acting. This creates missed opportunities and learning delays while competitors gain experience and market advantage.

An AI enablement playbook

Name your outcomes. Pick three measurable goals tied to customers, cost, or growth. Examples: reduce loan processing time by 30 percent, cut customer service response time from 4 hours to 30 minutes, or increase content production by 50 percent without adding headcount.

Map the work. List the steps where people read, write, search, decide, or hand off. These are all in AI’s wheelhouse to help. Look for tasks involving document review, email responses, data analysis, report generation, or quality checks.

Run small experiments. Two to four weeks. One team. One KPI. Ship something tangible and useful. Test AI-powered invoice processing with the accounting team, or AI-assisted internal help desk with support staff.

Measure and compare. Track speed, quality, cost, and satisfaction before and after. Keep what moves the needle. If AI cuts proposal writing time by 60 percent but reduces win rates by 20 percent, you need to adjust the approach.

Harden and scale. Add access controls, audit trails, curated prompt libraries, and playbooks. Move from a cool demo to a dependable tool that works consistently across teams and use cases.

Address the human element. Most resistance comes from fear of displacement, rather than technology aversion. Show people how AI handles routine tasks so they can focus on relationship building, creative problem-solving, and strategic work. Provide concrete examples of career advancement opportunities that AI creates.

Upskill your team. Short trainings with real tasks. Provide templates and examples in their daily tools. Make AI fluency a job requirement for new hires and a development goal for existing staff.

Close the loop with customers. Ask what improved. Watch behavior and survey scores, with extra weight on what people actually do, versus what they say.

Governance that speeds you up. Good guardrails create confidence and help you scale.

Access and roles. Limit sensitive data exposure and log usage by role. Marketing might get broad access to content generation tools while finance operates under stricter controls. The concept of least privilege applies. 

Data handling. Define red, yellow, and green data. Keep red data (customer SSNs, proprietary algorithms, confidential contracts) away from general public-facing tools. Yellow data needs approval and monitoring. Green data can flow freely.

Prompt and output standards. Save proven prompts in shared libraries. Require human review for customer-facing outputs, financial projections, or legal documents. Create templates that teams can adapt rather than starting from scratch.

Audit and monitoring. Capture prompts, outputs, and sources for key use cases. Build processes to detect bias, errors, or inappropriate content before it reaches customers.

Vendor review. Check security, uptime, and exit paths before heavy adoption. Understand data residency, model training practices, and integration capabilities. Consider making Bring-Your-Own-Key (BYOK) encryption the minimum standard for allowing your organization’s data to pass through or be stored on any AI vendor’s environment.

Questions for leaders

Which customer moments would benefit most from faster response or clearer guidance? Think about your highest-value interactions and biggest pain points.

Which workflows have the most repetitive reading or writing? These offer the quickest wins and clearest ROI calculations.

Which decisions would improve with better summaries or predictions? AI excels at processing large amounts of information and identifying patterns humans might miss.

Do we have the data infrastructure to support AI initiatives? Clean, accessible data is essential for most AI applications to work effectively. Solid data governance and curation are critical.

What risks must we manage as usage grows, and who owns that plan? Assign clear accountability for AI governance before problems emerge.

What will we stop doing once AI handles the routine? Define how you’ll reallocate human effort toward higher-value activities.

Who will champion AI adoption when the inevitable setbacks occur? Identify executives who understand both the potential and the challenges.

What to measure

Cycle time. Minutes or days saved per transaction.

Throughput. Work items per person per day.

Quality. Rework rate, error rate, compliance findings.

Experience. Customer effort score, employee satisfaction, NPS.

Unit cost. Cost per ticket, per claim, per application.

AI is the enabler

Strategy sets direction. AI supplies leverage. Give your people clear goals, safe guardrails, and permission to experiment and fail along the way.

Then let the tools do what tools do best. They multiply effort. They shorten the distance between intent and execution. They help you serve today’s customers better and reach customers you couldn’t reach in the past.

The question isn’t whether AI will transform your industry.

The question is whether you’ll lead that transformation or react to it.

Which will you choose?

Photo by Jen Theodore on Unsplash – I love this old school compass, showing the way as it always has. The same way a solid strategy and set of goals should lead our thinking about leveraging the latest AI tools.